Daily Anesthesiology Research Analysis

BACKGROUND: For pediatric pectus excavatum, the standard treatment is minimally invasive repair of pectus excavatum (MIRPE). A major challenge, however, is the severe postoperative pain. Although ultrasound-guided intercostal nerve block (UINB) offers effective analgesia, the technique's complexity and associated safety concerns are significant barriers, deterring its routine use. Modified intercostal nerve block (MINB) is effective in adult thoracic surgery but unvalidated in pediatric MIRPE. OBJECTIVE: To evaluate MINB's noninferiority to UINB for postoperative analgesia and safety in children undergoing MIRPE. DESIGN: Single-center randomized noninferiority trial. METHODS: Seventy-six ASA I-II pediatric patients (8-18 years) scheduled for single-bar MIRPE were 1:1 randomized to the MINB or UINB group. Primary outcome includes 24-h postoperative coughing visual analog scale (VAS) score (noninferiority margin Δ = 1.0). Secondary outcomes include resting/coughing VAS scores at 3, 6, 9, 12, 24, and 48 h postoperatively; procedure duration; local anesthetic dose; needle complications; opioid consumption; rescue analgesia; and adverse events. RESULTS: The mean difference in 24-h coughing VAS (MINB-UINB) score was -0.02 (95% CI: -0.85 to 0.80), confirming noninferiority of MINB (upper 95% CI limit 0.80 < noninferiority margin Δ = 1.0). MINB reduced procedure time by 65% (4.6 ± 1.3 vs. 13.2 ± 1.6 min; p < 0.001), decreased ropivacaine dose by 19% (50.0 ± 0.0 vs. 61.9 ± 4.6 mg; p < 0.001), shortened anesthesia duration (119.6 ± 18.3 vs. 131.8 ± 14.6 min; p = 0.002), and eliminated vascular injuries (0% vs. 16.2%; p = 0.025). All other outcomes demonstrated no statistically significant differences in the comparisons between the groups (p > 0.05). CONCLUSIONS: For children undergoing single-bar MIRPE, MINB provides noninferior analgesia to UINB with critical advantages: 65% faster placement, 19% lower ropivacaine dose, reduced anesthesia duration, and elimination of vascular injuries. These findings suggest that MINB offers a valuable alternative to UINB for post-MIRPE analgesia, as it appears to provide a more favorable balance between safety and efficiency. TRAIL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR2200057961.

Monitoring intracranial pressure (ICP) dynamics is critical for the management of traumatic brain injury, stroke, other neurosurgical conditions, and cerebral blood flow autoregulation; however, invasive ICP monitoring carries risks such as infection, hemorrhage, and sensor zero drift. Increasing evidence suggests that ICP waveform morphology provides clinically relevant information beyond mean ICP value alone. In this first-in-human prospective comparative clinical study, we evaluated the feasibility and accuracy of a novel, fully passive, non-invasive ICP pulse waveform monitoring system (Archimedes 02) based on the detection of eyeball mechanical movement. Fifteen intensive care unit patients (6 males, 9 females; mean age 57.1 ± 18.8 years) with clinically indicated invasive ICP monitoring or external ventricular drainage were enrolled. Three-minute monitoring sessions were performed to simultaneously acquire non-invasive ICP pulse waveforms, invasive ICP waveforms, and invasive radial artery blood pressure (ABP) waveforms. Averaged waveforms were derived for each patient and compared graphically and using correlation analysis. Non-invasive ICP pulse waves recorded with Archimedes 02 showed a strong correlation with invasive ICP waveforms (R¯ = 0.965). In contrast, correlations between non-invasive ICP and ABP waveforms (R¯ = 0.699), as well as between invasive ICP and ABP waveforms (R¯ = 0.749), were lower. These findings indicate that the non-invasive signal primarily reflects ICP dynamics rather than arterial blood pressure. This novel non-invasive ICP monitoring approach has the potential to enhance neurocritical care, particularly in settings where invasive monitoring is impractical or unavailable. Further validation in larger and more diverse patient populations is warranted.

BACKGROUND: Postoperative respiratory failure (PRF) is a severe complication after open-heart surgery, associated with increased mortality and prolonged ICU stays. While machine learning (ML) models have shown promise in predicting PRF, existing models often rely on fragmented data and lack interpretability. This study aimed to develop an interpretable ML model for early prediction of PRF using data from the first 24 h of ICU admission. METHODS: We analyzed data from the MIMIC-IV database, focusing on patients undergoing open-heart surgery with cardiopulmonary bypass (CPB). Patients with preoperative respiratory failure or significant missing data were excluded. Missing values (< 30%) were imputed using Predictive Mean Matching. Twelve features were selected through LASSO regression. We compared the performance of eight ML models using AUROC, AUPRC, and other metrics. The optimal model was further interpreted using Shapley Additive exPlanations (SHAP). RESULTS: Of the 4,488 patients, 339 (7.6%) developed PRF. The Gradient Boosting Machine (GBM) model demonstrated the best performance with an AUROC of 0.808, AUPRC of 0.369, and Youden's index of 0.479, indicating balanced sensitivity (0.703) and specificity (0.776). SHAP analysis revealed that key predictors included minimum ionized calcium levels, vasopressor score, and central venous oxygen saturation (ScvO₂), with their impact varying across patient risk categories. CONCLUSION: The GBM model, selected for its balanced performance across discrimination, calibration, and validation stability, provides a promising tool for early PRF risk stratification. The use of SHAP analysis enhances the interpretability of the model, highlighting the role of hemodynamic and metabolic markers in predicting PRF, thus improving clinical understanding and decision-making.